Breast Cancer Response Prediction in Neoadjuvant Chemotherapy Treatment Based on Texture Analysis

peer reviewe

Breast Cancer Response Prediction in Neoadjuvant Chemotherapy Treatment Based on Texture Analysis

peer reviewe

Breast cancer heterogeneity analysis as index of response to treatment using MRI images: A review

peer reviewedPurpose: Breast tumor structure contains a high degree of heterogeneity. This heterogeneity has been correlated with the level of tumor response to neoadjuvant chemotherapy. A significant number of studies using magnetic resonance imaging have looked into the quantification of intra tumor heterogeneity in breast cancer. Nevertheless, a limited number of these studies have specifi-cally looked at evaluating breast cancer heterogeneity as a biomarker of response to treatment. Methods: In this paper, several heterogeneity quantification methods, which have studied breast cancer response to treatment through MR images, will be resented. Important methodological and technological techniques using experimental design, such as histogram analysis, texture analysis and parametric response mapping (PRM), will be explored. Results: Data acquisition, the number of patients, the number of treatment cycles, and other anal-ysis will be discussed for each presented research case. Furthermore, some proposed methods will be evaluated on our institution MRI dataset, collected between 2013 and 2016. Conclusion: This paper can be used as a guideline for investigators working on breast cancer het-erogeneity as a biomarker of response to treatment

Deep learning-based prediction of response to HER2-targeted neoadjuvant chemotherapy from pre-treatment dynamic breast MRI: A multi-institutional validation study

Predicting response to neoadjuvant therapy is a vexing challenge in breast cancer. In this study, we evaluate the ability of deep learning to predict response to HER2-targeted neo-adjuvant chemotherapy (NAC) from pre-treatment dynamic contrast-enhanced (DCE) MRI acquired prior to treatment. In a retrospective study encompassing DCE-MRI data from a total of 157 HER2+ breast cancer patients from 5 institutions, we developed and validated a deep learning approach for predicting pathological complete response (pCR) to HER2-targeted NAC prior to treatment. 100 patients who received HER2-targeted neoadjuvant chemotherapy at a single institution were used to train (n=85) and tune (n=15) a convolutional neural network (CNN) to predict pCR. A multi-input CNN leveraging both pre-contrast and late post-contrast DCE-MRI acquisitions was identified to achieve optimal response prediction within the validation set (AUC=0.93). This model was then tested on two independent testing cohorts with pre-treatment DCE-MRI data. It achieved strong performance in a 28 patient testing set from a second institution (AUC=0.85, 95% CI 0.67-1.0, p=.0008) and a 29 patient multicenter trial including data from 3 additional institutions (AUC=0.77, 95% CI 0.58-0.97, p=0.006). Deep learning-based response prediction model was found to exceed a multivariable model incorporating predictive clinical variables (AUC < .65 in testing cohorts) and a model of semi-quantitative DCE-MRI pharmacokinetic measurements (AUC < .60 in testing cohorts). The results presented in this work across multiple sites suggest that with further validation deep learning could provide an effective and reliable tool to guide targeted therapy in breast cancer, thus reducing overtreatment among HER2+ patients.Comment: Braman and El Adoui contributed equally to this work. 33 pages, 3 figures in main tex

Breast Cancer Response Prediction in Neoadjuvant Chemotherapy Treatment Based on Texture Analysis

MRI modality is one of the most usual techniques used for diagnosis and treatment planning of breast cancer. The aim of this study is to prove that texture based feature techniques such as co-occurrence matrix features extracted from MRI images can be used to quantify response of tumor treatment. To this aim, we use a dataset composed of two breast MRI examinations for 9 patients. Three of them were responders and six non responders. The first exam was achieved before the initiation of the treatment (baseline). The later one was done after the first cycle of the chemo treatment (control). A set of selected texture parameters have been selected and calculated for each exam. These selected parameters are: Cluster Shade, dissimilarity, entropy, homogeneity. The p-values estimated for the pathologic complete responders PCR and non pathologic complete responders pNCR patients prove that homogeneity (P-value=0.027) and cluster shade (P-value=0.0013) are the more relevant parameters related to pathologic complete responders PCR.SCOPUS: cp.pinfo:eu-repo/semantics/publishe

Multi-input deep learning architecture for predicting breast tumor response to chemotherapy using quantitative MR images

Purpose: Neoadjuvant chemotherapy (NAC) aims to minimize the tumor size before surgery. Predicting response to NAC could reduce toxicity and delays to effective intervention. Computational analysis of dynamic contrast-enhanced magnetic resonance images (DCE-MRI) through deep convolution neural network (CNN) has shown a significant performance to distinguish responders and no responder’s patients. This study intends to present a new deep learning (DL) model predicting the breast cancer response to NAC based on multiple MRI inputs. Methods: A cohort of 723 axial slices extracted from 42 breast cancer patients who underwent NAC therapy was used to train and validate the developed DL model. This dataset was provided by our collaborator institute of radiology in Brussels. Fourteen external cases were used to validate the best obtained model to predict pCR based on pre- and post-chemotherapy DCE-MRI. The model performance was assessed by area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and feature map visualization. Results: The developed multi-inputs deep learning architecture was able to predict the pCR to NAC treatment in the validation dataset with an AUC of 0.91 using combined pre- and post-NAC images. The visual results showed that the most important extracted features from non-pCR tumors are in the peripheral region. The proposed method was more productive than the previous ones. Conclusion: Even with a limited training dataset size, the proposed and developed CNN model using DCE-MR images acquired before and after the first chemotherapy was able to classify pCR and non-pCR patients with substantial accuracy. This model could be used hereafter in clinical analysis after its evaluation based on more extra data.SCOPUS: ar.jDecretOANoAutActifinfo:eu-repo/semantics/publishe

A PRM approach for early prediction of breast cancer response to chemotherapy based on registered MR images

Purpose: This study aims to provide and optimize a performing algorithm for predicting the breast cancer response rate to the first round of chemotherapy using Magnetic Resonance Imaging (MRI). This provides an early recognition of breast tumor reaction to chemotherapy by using the Parametric Response Map (PRM) method. Methods: PRM may predict the breast cancer response to chemotherapy by analyzing voxel-by-voxel temporal intra-tumor changes during one round of chemotherapy. Indeed, the tumor recognizes intra-tumor changes concerning its vascularity, which is an important criterion in the present study. This method is mainly based on spatial image affine registration between the breast tumor MRI volumes, acquired before and after the first cycle of chemotherapy, and region growing segmentation of the tumor volume. To evaluate our method, we used a retrospective study of 40 patients provided by a collaborating institute. Results: PRM allows a color map to be created with the percentages of positive, negative and stable breast tumor response during the first round of chemotherapy, identifying each region with its response rate. We assessed the accuracy of the proposed method using technical and medical validation methods. The technical validation was based on landmarks-based registration and fully manual segmentation. The medical evaluation was based on the accuracy calculation of the standard reference of anatomic pathology. The p-values and the Area Under the Curve (AUC) of the Receiver Operating Characteristics were calculated to evaluate the proposed PRM method. Conclusion: We performed and evaluated the proposed PRM method to study and analyze the behavior of a tumor during the first round of chemotherapy, based on the intra-tumor changes of MR breast tumor images. The AUC obtained for the PRM method is considered as relevant in the early prediction of breast tumor response.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

A Texture Analysis Approach for Spine Metastasis Classification in T1 and T2 MRI

This paper presents a learning based approach for the classification of pathological vertebrae. The proposed method is applied to spine metastasis, a malignant tumor that develops inside bones and requires a rapid diagnosis for an effective treatment monitoring. We used multiple texture analysis techniques to extract useful features from two co-registered MR images sequences (T1, T2). These MRIs are part of a diagnostic protocol for vertebral metastases follow up. We adopted a slice by slice MRI analysis of 153 vertebra region of interest. Our method achieved a classification accuracy of 90.17% ± 5.49, using only a subset of 67 relevant selected features from the initial 142.SCOPUS: cp.kinfo:eu-repo/semantics/publishe

Neovascularization of lung carcinoma originating from single coronary artery

SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Plaque and stent artifact reduction in subtraction CT angiography using nonrigid registration and a volume penalty

Computed tomography angiography (CTA) is an established tool for vessel imaging. Yet, high-intense structures in the contrast image can seriously hamper luminal visualisation. This can be solved by subtraction CTA, where a native image is subtracted from the contrast image. However, patient and organ motion limit the application of this technique. Within this paper, a fully automated intensity-based nonrigid 3D registration algorithm for subtraction CT angiography is presented, using a penalty term to avoid volume change during registration. Visual and automated validation on four clinical datasets clearly show that the algorithm strongly reduces motion artifacts in subtraction CTA. With our method, 39% to 99% of the artifacts disappear, also those caused by minimal displacement of stents or calcified plaques. This results in a better visualisation of the vessel lumen, also of the smaller vessels, allowing a faster and more accurate inspection of the whole vascular structure, especially in case of stenosis.Loeckx D., Drisis S., Maes F., Vandermeulen D., Marchal G., Suetens P., ''Plaque and stent artifact reduction in subtraction CT angiography using nonrigid registration and a volume penalty'', Lecture notes in computer science, vol. 3750, pp. 361-368, 2005 (Proceedings 8th international conference on medical image computing and computer assisted intervention - MICCAI 2005, October 26-29, 2005, Palm Springs, California, USA).status: publishe